Digital Entrepreneurship in Platform-Based Economies: Opportunities and Challenges
DOI:
https://doi.org/10.68337/cpsm.v1.i2.2026-036Keywords:
Digital entrepreneurship, random forest, support vector machines, logistic regression, opportunities, customer-centricAbstract
This study examines digital entrepreneurship in platform-based economies, using machine learning (ML) to evaluate and predict entrepreneurial success. Random forest, support vector machine (SVM), and logistic regression models were trained on structured platform data covering customer ratings, engagement, revenue growth, and transaction frequency. Random forest performed best on every metric, with 88% accuracy and an area under the receiver operating characteristic curve (ROC-AUC) of 95%, reflecting its ability to model nonlinear relationships in platform ecosystems. Feature importance analysis identified customer-centric and engagement-related factors as critical for success, and cross-validation and hyperparameter tuning confirmed the stability of the results. ML techniques can give entrepreneurs and stakeholders data-driven insight into how digital platforms affect business outcomes.
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The data supporting the findings of this study are available from the corresponding author upon reasonable request.Conference Proceedings Volume
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